22 citations · 39 across the 5 of their papers we have counts for
8 papers
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection
Xin Li, Botian Shi, Yuenan Hou +4
Multi-modal 3D object detection has been an active research topic in autonomous driving. Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D…
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
Peishan Cong, Xinge Zhu, Feng Qiao +7
Accurately detecting and tracking pedestrians in 3D space is challenging due to large variations in rotations, poses and scales. The situation becomes even worse for dense crowds w…
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification
Xiaohan Xing, Yuenan Hou, Hang Li +3
The amount of medical images for training deep classification models is typically very scarce, making these deep models prone to overfit the training data. Studies showed that know…
Network Pruning via Resource Reallocation
Yuenan Hou, Zheng Ma, Chunxiao Liu +2
Channel pruning is broadly recognized as an effective approach to obtain a small compact model through eliminating unimportant channels from a large cumbersome network. Contemporar…
Inter-Region Affinity Distillation for Road Marking Segmentation
Yuenan Hou, Zheng Ma, Chunxiao Liu +2
We study the problem of distilling knowledge from a large deep teacher network to a much smaller student network for the task of road marking segmentation. In this work, we explore…
Learning Lightweight Lane Detection CNNs by Self Attention Distillation
Yuenan Hou, Zheng Ma, Chunxiao Liu +1
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations. Without learning from much richer context…